Skip to main content
The Actuary: The magazine of the Institute and Faculty of Actuaries - return to the homepage Logo of The Actuary website
  • Search
  • Visit The Actuary Magazine on Facebook
  • Visit The Actuary Magazine on LinkedIn
  • Visit @TheActuaryMag on Twitter
Visit the website of the Institute and Faculty of Actuaries Logo of the Institute and Faculty of Actuaries

Main navigation

  • News
  • Features
    • General Features
    • Interviews
    • Students
    • Opinion
  • Topics
  • Knowledge
    • Business Skills
    • Careers
    • Events
    • Predictions by The Actuary
    • Whitepapers
    • Moody's - Climate Risk Insurers series
    • Webinars
    • Podcasts
  • Jobs
  • IFoA
    • CEO Comment
    • IFoA News
    • People & Social News
    • President Comment
  • Archive
Quick links:
  • Home
  • The Actuary Issues
  • November 2020
General Features

Perfect match: automating the identification of asset characteristics for Solvency II matching adjustment

Open-access content Wednesday 4th November 2020
Authors
Rachael Armitage
Simone Bohnenberger-Rich

Rachael Armitage and Simone Bohnenberger-Rich discuss how to automate the identification of asset characteristics for the Solvency II matching adjustment

Perfect match: automating the identification of asset characteristics for Solvency II matching adjustment

Since its emergence in the 1500s, the UK life insurance industry has been a keen adopter of technologies. The earliest life insurance policy is believed to be dated 1583 and covers the life of a William Gibbons. At that time, technology included the abacus – one of the earliest calculating devices.

The 1700s saw the introduction of logarithms and mortality tables, while the 1800s brought machines and devices to assist calculation. Electrically operated machines began to appear in actuarial departments in the 1930s, but it wasn’t until the early 1960s that we saw the first electronic desk calculators in actuarial offices.

 Less than 60 years later, a life insurance office without technology would be unrecognisable. Life insurers are now looking to embrace artificial intelligence (AI) and machine learning (ML), to the extent that they can benefit from them. Since life insurers are required to process text in addition to numerical data, documentation stands out as being ripe for the application of these smart technologies.

Solvency II matching adjustment and documentation

The Solvency II matching adjustment (MA) allows firms to adjust the risk-free interest rate used to calculate the best estimate of a portfolio of eligible insurance liabilities. The allowable adjustment is based on assets that meet the MA criteria set by the regulator.

Identifying such assets requires extracting asset characteristics directly from product prospectuses and associated publications, such as pricing supplements. These documents are not always in electronically readable formats, and can be lengthy.  

The project

A technology-focused and forward-thinking asset manager wanted to explore the use of smart technology to determine whether certain bonds are eligible for MA. The pilot was an assessment of 1,000 bonds. We believe this is one of the first adoptions of smart technology in the life insurance industry and in Solvency II.

AI and ML have come on a long way over the last couple of years, and can now help with nuanced tasks as well as repetitive tasks. ML is focused on developing systems that can ‘learn’ patterns from data and using those patterns to make predictions when presented with new data. In this example, ML has been applied to the document review needed to determine MA eligibility.

Natural Language Processing (NLP) applies this predictive ability to human language. In this example, users can show an NLP platform examples of bond prospectuses and direct the platform to the information they want to surface from those prospectuses, such as maturity date. The platform then builds patterns from these examples, which allows it to predict and produce information when shown new bond prospectuses. 

Perfect match

Perfect match
Training the platform 

Figure 1 illustrates the simplified workflow of this project. As a first step, we worked to define what information is required from bond prospectuses to assess whether a bond is eligible for MA. Determining MA eligibility is nuanced, and an assessment needs to consider multiple data points. A provision or characteristic that disrupts, or calls into question, certainty of the cashflow profile of the bond needs to be understood. Example data points include maturity date, coupon timing or call option applicability.

Once these data points were identified, the NLP platform was trained on a sample of bonds, teaching it how to find the information required to determine eligibility (2).  

Next, the platform created patterns based on the training set (3) so that it could predict the required data for additional bonds. After the training of the machine was complete (4), around 3,000 documents were uploaded for the platform to analyse (5), from which it extracted, bond-by-bond, the information required, as shown in Figure 2. 

Finalising the results

At this stage, human intervention is critical. The NLP platform flags data points about which it is uncertain (6) and these should be reviewed, checked and, if necessary, corrected by a human. In addition, data points that the actuarial analysis team recognised as unlikely to be correct were reviewed and, if necessary, corrected. ‘Flagging’ of low-confidence answers brings together the best of both worlds: human and platform together produce more accurate results than human alone or platform alone.

Impact   

ML is not a silver bullet. It does not produce results with 100% accuracy – but nor do humans. NLP in the application of MA review can be a time saver and promote accuracy. Data gathered from large-scale exercises, where the platform has reviewed hundreds of thousands of documents and been benchmarked against human performance, shows that human accuracy is around 70%–80%. The human brain is not designed for repetitive tasks, which is why human accuracy falls below 100%.  

There is enormous power in combining the strengths of ML with human review to get to a near-perfect data set. In particular, this project has demonstrated that NLP, together with actuarial and ML expertise, can drive efficiency and enable repetitive tasks associated with MA eligibility to be outsourced, in part, to a machine.

Rachel and Simone will be presenting their webinar ‘AI in an actuarial world: training a machine to assess matching adjustment eligibility’ at this year’s Life Conference. Visit www.actuaries.org.uk/Life2020

Rachael Armitage is a director in Deloitte's Actuarial Life Insurance practice

Simone Bohnenberger-Rich is head of product portfolio at Eigen Technologies

Image Credit | iStock
 
ACT Nov20_Full.jpg
This article appeared in our November 2020 issue of The Actuary .
Click here to view this issue

You may also be interested in...

On the right tracks: the Pensions Regulator’s consultation

On the right tracks: the Pensions Regulator’s consultation

The UK Pensions Regulator’s (TPR) consultation on a new Code of Practice for funding defined benefits (DB) pension schemes has just closed.
Wednesday 4th November 2020
Open-access content
web_20_HIRES-p30-31 Illustration of man sprinting hurdles with wad of cash  Ikon  17864.jpg

Investment funds: charging ahead

Brandon Horwitz looks at assessing value for money for investment funds, and how remedies adopted by the FCA aim to build on a duty to act in the best interests of investors
Wednesday 4th November 2020
Open-access content
Adding experts to injury

Adding experts to injury

Peter Wylie looks at the history of injury damage decisions and the involvement of actuarial calculations in court decisions
Wednesday 4th November 2020
Open-access content
Completing the matrix

Completing the matrix

Correlation matrices arise in many applications to model the dependence between variables. Dan Georgescu and Nick Higham ask what happens when we have a partially specified matrix and we wish to fill in the missing elements
Wednesday 4th November 2020
Open-access content
Field goals

Investment risk: Clarity out of confusion

Once a year, UK investors receive a statement on the performance of their investments and pensions –  but the information provided is often shamefully baffling and incomprehensible.
Wednesday 4th November 2020
Open-access content

India's crop insurance schemes: Field goals

“The introduction of PMFBY brought some much-needed pricing correction and enhanced the benefit’s scope, bringing millions of additional farmers under insurance coverage”
Wednesday 4th November 2020
Open-access content

Latest from General Features

yguk

Is anybody out there?

There’s no point speaking if no one hears you. Effective communication starts with silence – this is the understated art of listening, says Tan Suee Chieh
Thursday 2nd March 2023
Open-access content
ers

By halves

Reducing the pensions gap between men and women is a work in progress – and there’s still a long way to go, with women retiring on 50% less than men, says Alexandra Miles
Thursday 2nd March 2023
Open-access content
web_Question-mark-lightbulbs_credit_iStock-1348235111.png

Figuring it out

Psychologist Wendy Johnson recalls how qualifying as an actuary and running her own consultancy in the US allowed her to overcome shyness and gave her essential skills for life
Wednesday 1st March 2023
Open-access content

Latest from November 2020

People and society news: November

People and society news: November

People and society news: November
Wednesday 4th November 2020
Open-access content
Guiding lights

Guiding lights: non-executive directors

Guiding lights: non-executive directors
Wednesday 4th November 2020
Open-access content
Far-reaching crisis

Far-reaching crisis

Darshan Purmessur
Wednesday 4th November 2020
Open-access content
Share
  • Twitter
  • Facebook
  • Linked in
  • Mail
  • Print

Latest Jobs

Capital & Reserving, Nearly Newly

London (Greater)
Depending on experience
Reference
149031

Reserving Actuary

Dublin
Competitive
Reference
149027

Senior Analyst - Actuarial and Funding Risk

England, London
£60000 - £65000 per annum + bonus + benefits
Reference
149029
See all jobs »
 
 
 
 

Sign up to our newsletter

News, jobs and updates

Sign up

Subscribe to The Actuary

Receive the print edition straight to your door

Subscribe
Spread-iPad-slantB-june.png

Topics

  • Data Science
  • Investment
  • Risk & ERM
  • Pensions
  • Environment
  • Soft skills
  • General Insurance
  • Regulation Standards
  • Health care
  • Technology
  • Reinsurance
  • Global
  • Life insurance
​
FOLLOW US
The Actuary on LinkedIn
@TheActuaryMag on Twitter
Facebook: The Actuary Magazine
CONTACT US
The Actuary
Tel: (+44) 020 7880 6200
​

IFoA

About IFoA
Become an actuary
IFoA Events
About membership

Information

Privacy Policy
Terms & Conditions
Cookie Policy
Think Green

Get in touch

Contact us
Advertise with us
Subscribe to The Actuary Magazine
Contribute

The Actuary Jobs

Actuarial job search
Pensions jobs
General insurance jobs
Solvency II jobs

© 2023 The Actuary. The Actuary is published on behalf of the Institute and Faculty of Actuaries by Redactive Publishing Limited. All rights reserved. Reproduction of any part is not allowed without written permission.

Redactive Media Group Ltd, 71-75 Shelton Street, London WC2H 9JQ